http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/420 2026-04-06T20:56:16Z 2026-04-06T20:56:16Z Roy, Indrani http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/4795 2026-03-03T07:45:46Z 2026-03-03T00:00:00Z

Securing Graphical Authentication Using Keystroke Dynamics Roy, Indrani Account recovery is a critical aspect of web application security, often overlooked despite its importance. Traditional account recovery methods, such as sending a password reset link or a new username to the user’s registered email, are vulnerable to impostors who may have access to the user’s email and other credentials. This vulnerability makes account recovery a potential weak point in the overall security of a web application. Recent applications of behavioral biometrics, such as keystroke dynamics, for attack detection and user authentication bear similarities to biometric authentication. Adding keystroke dynamics analysis to the account recovery process significantly increases the difficulty for an impostor to successfully recover and take over a user’s account. To enhance user authentication effectiveness and raise account recovery requirements through keystroke dynamics, this study adds one additional measure of keystroke patterns to the already-existing features. Compared to other access control systems based on biometric features like face or fingerprint, keystroke analysis has attained a respectable level of accuracy. In this aim, this study uses experimental data and statistical analysis to show how the unique keystroke measure provided may be utilized in conjunction with the current authentication mechanism to greatly improve the authentication and security of sensitive applications. It may be beneficial to recognize the intruders and expel them from the system as long as this job can accommodate their typing rhythm. In this study, generative adversarial networks (GAN) are utilized to generate keyboard dynamics data with a focus on impersonating a user at the identification step in both fixed text and fixed sentence contexts. Three distinct architectures have been devised, implemented, and validated with the aid of machine learning and deep learning: vanilla-GAN based on simple neural networks NN, LSTM-GAN based on recurrent neural networks using long short-term memories (LSTM), CNN-GAN based on convolutional neural networks. The developed Conditional Generative Adversarial Networks have shown that these architectures can successfully replicate a user’s keystroke dynamics by learning about the user’s typing style and generating keyboard dynamics data using different GANs with different architectural styles. Findings show that keystroke dynamics patterns can be efficiently produced by the GAN and utilized to trick keystroke authentication systems. This thesis is submitted for the degree of Master of Philosophy.

2026-03-03T00:00:00Z Jabiullah, M. Ismail http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/4661 2025-05-27T04:36:20Z 2025-05-27T00:00:00Z

Session keys for secured electronic transactions Jabiullah, M. Ismail This thesis is submitted for the degree of Master of Philosophy.

2025-05-27T00:00:00Z Khan, Taskina http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/3896 2025-03-11T09:24:49Z 2025-03-11T00:00:00Z

Solar heated hot water systems Khan, Taskina This thesis is submitted for the degree of Master of Philosophy.

2025-03-11T00:00:00Z Karim, Masuda http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/424 2019-10-07T09:55:23Z 2019-10-07T00:00:00Z

User Authentication from Mouse Movement Data Using Multi Classifier Karim, Masuda User authentication is a process to verify the identity of someone who connects to system or resource. There are many technologies to authenticate a user. The biometric authentication system is becoming very popular due to its unique characteristics. This thesis presents a user authentication system from the mouse movement data. The mouse movement data are captured using our own developed user interface and two tools named Jitbit macro reader and Recording User Input (RUI). Raw data are sampled into blocks in two ways: one is based on specific number of action and another is based on specific duration. These data blocks are stored in database. From each blocks, twelve features are generated: Number of Points in the Trajectory, Delay Time, Number of Delay, Number of Action, Standard Deviation of Trajectory Length, Total Length of Trajectory, Standard Deviation of Slope, Standard Deviation of Difference Between Each of Slopes, Number of Curvatures, Curvature of Trajectory, Number of Changes in Horizontal Position and Number of Changes in Vertical Position. This system uses three classifiers: Support Vector Machine, K-Nearest Neighbor and Naïve Bayes separately to verify the proposed authentication system. The system is trained and tested using our captured dataset of 10 users and a benchmark dataset of 28 users. The experimental result shows that K-Nearest Neighbor based classifier performs better in terms of Average Receiver Operating Characteristic (ROC) Area, False Acceptance Rate (FAR) and False Rejection Rate (FRR). We have found FAR=2.78 and FRR=0 by using our collected own data and FAR=1 and FRR=1.2 by using benchmark data. This system is compared with S. Suganya, G. Muthumari, and C. Balasubramanian’s research and found that both FAR and FRR is improved. This thesis submitted for the degree of Master of Philosophy in The University of Dhaka.

2019-10-07T00:00:00Z